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arXiv · 2505.22404

Efficient Precision-Scalable Hardware for Microscaling (MX) Processing in Robotics Learning

Stef Cuyckens·Xiaoling Yi·Nitish Satya Murthy·Chao Fang·Marian Verhelst
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Abstract

Autonomous robots require efficient on-device learning to adapt to new environments without cloud dependency. For this edge training, Microscaling (MX) data types offer a promising solution by combining integer and floating-point representations with shared exponents, reducing energy consumption while maintaining accuracy. However, the state-of-the-art continuous learning processor, namely Dacapo, faces limitations with its MXINT-only support and inefficient vector-based grouping during backpropagation. In this paper, we present, to the best of our knowledge, the first work that addresses these limitations with two key innovations: (1) a precision-scalable arithmetic unit that supports all six MX data types by exploiting sub-word parallelism and unified integer and floating-point processing; and (2) support for square shared exponent groups to enable efficient weight handling during backpropagation, removing storage redundancy and quantization overhead. We evaluate our design against Dacapo under iso-peak-throughput on four robotics workloads in TSMC 16nm FinFET technology at 400MHz, reaching a 51% lower memory footprint, and 4x higher effective training throughput, while achieving comparable energy efficiency, enabling efficient robotics continual learning at the edge.

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Stef Cuyckens, Xiaoling Yi, Nitish Satya Murthy, Chao Fang, Marian Verhelst. 2025-07-23. Efficient Precision-Scalable Hardware for Microscaling (MX) Processing in Robotics Learning. https://doi.org/10.1109/islped65674.2025.11261796

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